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Review of the Application of Water Pinch Technology in Water-saving and Emission Reduction Lihong Meng1, a, Qi Qiao1 and Jingyang Liu1 1 Chinese Research Academy of Environmental Sciences, Beijing 100012, China ayuduanhongri@163.com Keywords: water pinch, water-saving, emission reduction Abstract.
And its application on the water saving emission reduction of other industries is less.
Taking a petrochemical enterprise for example, through applications of software on analysis of water data of the maximum impurity concentration of system exports, optimized system with conventional analysis and system with water networks optimal design software, we could see that use of water pinch technology optimization saved water by 48.7% than original design 14.6% than conventional optimization technology.
Zhu Shenhua[18] compared the system water data of regular analysis optimization and water pinch technology optimization with that of original design, and found that water-saving rate of routine analysis optimization and water-pinch technology optimization were 17.62% and 31.03%, respectively.
Based on experience or data analysis solution as well as the parameter mentioned above, the water network is designed which may have deviation with regeneration concentration in actual operation.

The technologies bring with the building a reduction 6.5 kt CO2 each year.
The sustainable design of the building would have a reduction of about 6 kt CO2 annually.
It means that the solar PV of China pavilion contributes GHG reduction of 552 t CO2e.
The CO2 reduction attributable to the green measures would amount to 6.5 kt CO2 annually.
The authors would like to thank the Bureau of Shanghai World Expo Coordination and Shanghai Academy of Environmental Sciences for providing related data of the 2010 Expo Shanghai China as well as those anonymous reviewers of their helpful comments.

Hence, the reduction ratio of gear reducer is required to be higher and higher. 3K type and 2K-2H type planetary gear trains can be designed to have high reduction ratios.
This paper proposes 2K type planetarygear train with high reduction ratio.
(a) Reduction ratio Rr=40 (b) Reduction ratio Rr=50 (c) Reduction ratio Rr=100 Fig.4. 2K type planetary gear reducers with simple planet gears The Gear Data for 2K type Planetary Simple Gear Reducer In follows, the 2K typeplanetary simple gear reducer shown in Fig. 4(a) will be the example to illustrate how to design the two ring gears with different shift coefficient to engage the same planet gear.
Table 1 show the gear data of 2K type planetary simple gear reducer shown in Fig.4(a).
Table 1, Gear data of planetary simple gear reducer shown in Fig. 4(a) Planet gear 3 Ring gear 4 Ring gear 5 Teeth No. 16 38 40 Normal Module (mm) 2.0 2.0 2.0 Shift coefficient (mm) 0.2 0.58 -0.18 Pitch Dia.

Electrocatalytical reduction of CO2; Recycling utilization of CO2.
Our aim is to provide the basic data for fundamental research of CO2 electrochemical reduction.
In previous work, many researcher selected Ag electrode as the cathode for CO2 electrochemical reduction to suppress hydrogen evolution and improve the Faradaic efficiency of CO2 reduction.
The increase of the current density is attributed to the reduction of CO2.
Anagnostopoulos, Electrochemical reduction of CO2 at Cu + Au electrodes, J.

Therefore sulfide hinders the cathodic reduction of O2 on Q235 carbon steel in 3.5% NaCl solution.
The results of EIS were analyzed with fitting the data using Zsimpwin software.
For N2-saturated solution, hydrogen evolution reaction (HER) occurs at potentials more negative than-1.20 V (Fig. 1a), and the reduction peak at −1.15V is attributed to iron oxides reduction[15].
The peak at -0.93V and −1.15V are attributed to ORR and iron oxides reduction, respectively.
The variation of electrode surface restrains the reduction reaction of O2 and iron oxides.

The data are collected in time-snapshots denoted by ηi= η(ti,w1)…η(ti,wn)∈Rn, i=1,…,N (1) One looks for a set of orthonormal basis vectors uj∈Rn, j=1,2,…,n , such that ηi=j=1nγjiuj , with i=1, 2,…,N , that is, η1…ηN=u1…unγ11…γ1N⋮⋱⋮γn1…γnN, U*U=In (2) where In∈Rn x n is the identity matrix, Η=η1…ηN, U=u1…un and * is the (conjugate) transpose operator.
The uj are referred to as empirical eigenfunctions or principal direction of the “cloud” of data ηi.
As a second step, a reduction of n is also investigated.
Up to now local linearizations were adopted to support the model order reduction.
Model Order Reduction: Theory, Research Aspects and Applications, Springer, 2008

JPEG Steganalysis Based on Locality Preserving Projection Dimensionality Reduction Method Zhu Tingting 1,2,3 a, Wang Lina 1,3, Fu Yu2 and Ren Yanzhen 1,3 1 Computer School, Whuhan University, Whuhan, Hubei 430079, China 2 Department of Information Security, Naval University of Engineering, Whuhan, Hubei 430033, China 3 The Key Laboratory of Aerospace Information Security and Trust Computing, Ministry of Education, Wuhan, Hubei 430079, China a zlztt1688@sina.com.cn Keywords: Steganalysis; Co-occurrence matrix; Support Vector Data Description (SVDD); Locality preserving projection (LPP) Abstract.
In this paper, a JPEG steganalysis algorithm based on locality preserving projection (LPP) dimensionality reduction method is proposed for detecting the unseen stego algorithms.
In section 3, the algrithm of feature dimensionality reduction, which use LPP method, is described.
The steganalysis features are as follows: (18) 3 Features Dimensionality Reductions Based on LPP The Locality Preserving Projection (LPP) is a linear approximation of the nonlinear Laplacian Eigenmap [5] .It is an unsupervised technique which is always used to reduce the dimension of feature and aims to preserve the local neighborhood structure of the patterns after the linear dimensionality reduction.
Summary For detecting the unseen stego algorithms and reduce the feature dimension, a JPEG steganalysis algorithm based on locality preserving projection (LPP) dimensionality reduction method is proposed.

In order to get better PAPR reduction performance, we propose a new PAPR reduction algorithm based on parametric minimum cross entropy(PMCE).
PAPR Reduction Using Hexagonal Constellation And Parametric Minimum Cross Entropy (PMCE) Algorithm Let us denote the data block of length as a vector , where is the number of subcarriers.
For each data block, denote the number and index set of subcarriers where constellation points have two representations in hexagonal constellations as and respectively, where .
Comparison of PAPR reduction by different methods.
In order to get better PAPR reduction performance, we propose a new PAPR reduction algorithm based on parametric minimum cross entropy (PMCE).

This data shows that higher reduction temperature resulted in higher formation of wustite and magnetite.
While Figure 6 represents the result of reduction process with the lowest reduction parameter.
The data shows that wustite tends to diminish as process duration increase.
Data resulting from step 1 shows that the highest formation of wustite is happened in reduction temperature of 1000oC with 1:2 weight ratio; the highest formation of magnetite is happened in reduction temperature of 800oC with 1:4 weight ratio.
Data resulting from step 2 shows that wustite highest formation is happened in reduction temperature of 1000oC with 30 minutes process duration and magnetite highest formation is in reduction temperature of 700oC with 30 minutes process duration.

Attribute reduction is a key problem for rough set theory.
The actual application shows that the method is feasible and effective Introduction The theory of rough set proposed by Pawlak [1] in 1982 is a new method for data processing based on equivalence relation, and it has been successfully applied in such artificial intelligence fields as machine learning, pattern recognition, decision analysis, process control, knowledge discovery in databases and expert systems.
In Section 4, we show some example on fourteen public data sets.
Information System and Decision Table At Rough sets have been employed to remove redundant conditional attributes from discrete-valued data sets while retaining their information content.
This method does not always generate a minimal reduct, but it does result in a close-to-minimal reduct, which is still useful in reducing data set dimensionality.